Example of CA : SLEUTH, a more complex one

Now we shortly present a CA model proposed by Clarke et al. (1998). This CA simulation model was developed to predict urban growth in the San Francisco Bay area. They have used multiple data sources: a topography map, historical maps of highway development from 1920 to 1978, existing settlement distributions and their modification over time.

The SLEUTH name is the acronym for Slope, Landuse, Exclusion, Urban, Transportation and Hillshading. These are factors controlling the urban growth process.

The control parameters of the model are allowed to self-modify, where the CA adapts to the circumstances it generates, especially, during periods of rapid growth or stagnation. The model accumulated probabilistic estimates based on Monte Carlo methods.

The next figure illustrates the four input data layers used by termSLEUTH model for simulating urban growth:

                          The four input data layers used by SLEUTH model for simulating urban growth (after Clarke et al., 1997) The four input data layers used by SLEUTH model for simulating urban growth (after Clarke et al., 1997)


There are five factors that control the behaviour of the system, which are growth control parameters in the model:


The values for DIFFUSION, BREED, SPREAD, and SLOPE-RESISTANCE range from 0-100, and ROAD-GRAVITY ranges from 0-20.

Growth rules: model operation for a single cycle, a year for example (after Clarke et al., 1997)Growth rules: model operation for a single cycle, a year for example (after Clarke et al., 1997)

As we can see from the last figure illustrating the four growth rules, the model defines the growth rate as the sum of the four types of urban growth, which are:


To allow the model to modify itself, a new set of rules are defined by coupling the variables. These modification rules can be summarised in four conditions:

  1. When the absolute amount of growth in any year (cycle) exceeds a critical value, the DIFFUSION, SPREAD, and BREED factors are increased by a multiplier greater than one
  2. When the system growth rate falls below another critical value, the DIFFUSION, SPREAD, and BREED factors are decreased by a multiplier less than one
  3. The ROAD-GRAVITY factor is increased as the road network expands
  4. The SLOPE-RESISTANCE factor is increased as the land available for development decreases


The following figure illustrates the self-modification rules.

                     Self-modification adjustments to the control parameters (after Clarke et al., 1997                     Self-modification adjustments to the control parameters (after Clarke et al., 1997

The SLEUTH model was applied to the study area of Bulle (Al-Ghamdi 2008) in order to simulate the urban development until the year 2050 according to three termscenarios of development with different levels of controlled expansion: extreme, moderate and less control scenario.

This development simulation requires two main stages: the calibration stage used to train and to evaluate the model capability based on past and documented LUC for the years 1952, 1974, 1993 and 2001, followed by the prediction stage that evaluates the urban extent for future and undocumented dates.

termCalibration stage: five following layers that express the input factors controlling the urban growth process were introduced for the control years 1952, 1974, 1993 and 2001: LUC, urban extent, road network, excluded areas and slope. During this stage a validation phase was realised in order to estimate the capabilities to model the urban extent in 1993 and 2001. This model validation indicated a very satisfying simulation ability with a respective Kappa value of 0.98 and 0.95. The spatial distribution of projected and observed urban extent for 1993 and 2001 are illustrated in the next figure.

                      Projected and observed urban extent in Bulle area for 1993 and 2001 (after Al-Ghamdi, 2008)                     Projected and observed urban extent in Bulle area for 1993 and 2001 (after Al-Ghamdi, 2008)

termPrediction stage: mentioned above, this simulation is aimed to predict the urban development during the period 2001 to 2050. As the SLEUTH model authorises to modify rules and controlling factors during the iterative prediction stage, the road development plan for 2012 is added as an input layer as is the new planned protected areas layer (next table).

                      Input layers used for the prediction stage (adapted from Al-Ghamdi, 2008)                                             Input layers used for the prediction stage (adapted from Al-Ghamdi, 2008)

Three scenarios are submitted to forecast the urban development with different levels of controlled expansion: extreme, moderate and less control scenario. In the three scenarios all land use types are excluded from urbanisation, as having a control level value of 100 (Table), except for agriculture land that is left open if it is outside existing or new planned protected areas. A zero level of exclusion is then assigned to this land use type. The difference between the three scenarios is the following:

                         Three different scenarios and level of exclusion (adapted from Al-Ghamdi, 2008)                             Three different scenarios and level of exclusion (adapted from Al-Ghamdi, 2008)

The next slideshow illustrates the urban growth simulation during the period 2001-2050 according to the conditions and rules assigned to the three scenarios.

Urban growth simulation in Bulle area according to the three different scenarios during the period 2001 to 2050 (adapted from Al-Ghamdi, 2008)

Differences between the three scenarios at the end of the simulated period are illustrated in the next figure. One can observe the influence of the control level of exclusion on the spatial growth as well as the one from the road network.

                     Projected urban extent for 2050 according to the three scenarios and related with the road network and excluded areas (adapted from Al-Ghamdi, 2008).                     Projected urban extent for 2050 according to the three scenarios and related with the road network and excluded areas (adapted from Al-Ghamdi, 2008).

EXERCISE:

From the last try to visually estimate: